Activation of micro aluminum particles with non-thermal plasma for reaction with compressed water
Bibliographic record
Abstract
Oxidation of aluminum powder with water cogenerates hydrogen, heat, and valuable aluminum (hydr)oxides. However, initiating the oxidation reaction is inhibited by the protective aluminum oxide shell. In this work, non-thermal hydrogen and argon plasmas are used to activate the surface of 45 μm spherical aluminum particles prior to oxidation in compressed water at 1500 psig. It was found that non-thermal plasma treatment enhanced the oxidation kinetics such that ignition delay decreased by 45 % and rate of temperature increase at thermal runaway increased by 50 % compared to the untreated powders. Surface characterization of the aluminum powders via X-ray photoelectron spectroscopy and energy dispersive X-ray spectroscopy showed that oxygen-to-aluminum atomic concentration ratio decreased after plasma treatment, which indicates that the thickness of the aluminum oxide shell on the particle surface was reduced. Non-thermal plasma treatment is a chemical activation that improves the reactivity of aluminum powders in water instead of classical methods such as particle size reduction and oxidation in alkaline solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".